Optimized feature extraction by immune clonal selection algorithm

Abstract

A new method of feature extraction based on immune clonal selection algorithm is proposed, in which the immune clonal selection algorithm is used to optimize the projection vector. Some orthogonal bases are randomly selected as the initial basis vector sets from the original feature space, and the direction of the basis vectors is optimized to generate the optimal projection vector using the immune clonal selection algorithm. This method provides a new scheme of applying the immune clonal algorithm to feature extraction. Experimental results on benchmark datasets and MSTAR dataset for SAR target recognition verify the effectiveness of the proposed method.